2008 Presidential Campagin Finance for president Barack Obama by Alina Jurgensen
## cmte_id cand_id cand_nm
## C00431445:15243 P80003338:15243 Obama, Barack:15243
##
##
##
##
##
##
## contbr_nm contbr_city contbr_st
## KHV, KHV : 110 HONOLULU :6558 HI:15243
## HOUSTON, PATRICIA: 76 KAILUA :1202
## MURAKAMI, ROBERT : 61 HILO : 611
## YAMANOHA, LYN : 60 KANEOHE : 586
## SULTAN, A A : 58 KAMUELA : 517
## TYLER, CLARK : 58 KAILUA KONA: 435
## (Other) :14820 (Other) :5334
## contbr_zip contbr_employer contbr_occupation
## Min. : 0 NOT EMPLOYED :3738 RETIRED : 3403
## 1st Qu.:967343212 SELF EMPLOYED :2842 ATTORNEY : 620
## Median :967688422 UNIVERSITY OF HAWAII : 452 NOT EMPLOYED: 477
## Mean :882254074 INFORMATION REQUESTED: 325 PROFESSOR : 350
## 3rd Qu.:968165550 : 318 TEACHER : 340
## Max. :968480001 (Other) :7531 (Other) :10021
## NA's :3 NA's : 37 NA's : 32
## contb_receipt_amt contb_receipt_dt
## Min. :-4600.0 16-Oct-08: 313
## 1st Qu.: 25.0 30-Sep-08: 313
## Median : 75.0 27-Aug-08: 299
## Mean : 203.6 31-Jul-08: 282
## 3rd Qu.: 125.0 24-Oct-08: 281
## Max. : 4600.0 31-Oct-08: 238
## NA's :35 (Other) :13517
## receipt_desc memo_cd
## :14424 :14165
## REATTRIBUTION/REDESIGNATION REQUESTED: 266 X: 1078
## REDESIGNATION FROM : 252
## REDESIGNATION TO : 248
## Refund : 38
## REATTRIBUTION FROM : 5
## (Other) : 10
## memo_text form_tp
## :13040 : 35
## OVF TRANSFER : 1054 SA17A:14100
## REATTRIBUTION/REDESIGNATION REQUESTED: 266 SA18 : 1070
## ORIGINAL TRANSACTION : 256 SB28A: 38
## REDESIGNATION FROM : 252
## REDESIGNATION TO : 248
## (Other) : 127
## file_num tran_id election_tp
## Min. :359390 : 35 : 35
## 1st Qu.:753671 2299577 : 2 G2008:6418
## Median :753769 2299577RMIN: 2 P2008:8790
## Mean :658074 10000818 : 1
## 3rd Qu.:753821 10001490 : 1
## Max. :754317 10001522 : 1
## NA's :35 (Other) :15201Univariate Plots Section
My report explores the 2008 Presidential Campagin Finance dataset containing data about financial support to president Barack Obama, in his native state, Hawaii.
The majority of countributors come from Honolulu, followed by Kailua and Hilo.
I create a new category ‘County’ and I see that most contributors come from Honolulu county.
Most of the contributions are around 100, with 50% of the contributions greater than 75.
The primary election had received more contributions than the general.
Univariate Analysis
What is the structure of your dataset?
My dataset has 15243 observations and 18 variables. The most relevant variables for my report are: - contbr_nm: the contributer’s name - contbr_city: the contributer’s city - contb_receipt_amt: the amount - contbr_occupation - contbr_employer - election_tp: election type
What is/are the main feature(s) of interest in your dataset?
I would like to see the received financial support by looking at contributor’s location, so I choose two counties to explore: Honolulu and Hawaii. The variables used are contbr_city contb_receipt_amt and contbr_occupation.
What other features in the dataset do you think will help support your investigation into your feature(s) of interest?
I will use election_tp to compare the data between the two election types.
Did you create any new variables from existing variables in the dataset?
I created new categories for county, employer and occupation.
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?
Bivariate Plots Section
## $title
## [1] "Contribution amount by county"
##
## attr(,"class")
## [1] "labels"Due to their contrast, I chose to focus on 2 different counties: Honolulu and Hawaii.
## RETIRED DRAFTING SERVICE
## 255 59
## PROFESSOR NOT EMPLOYED
## 47 45
## TEACHER
## 42 35
## SCHOOL ADMINISTRATOR BUILDER
## 23 22
## COFFEE ROASTER SOFTWARE ENGINEER
## 19 19
## INNKEEPER AND COUNSELOR ATTORNEY
## 18 17
## FINANCIAL SOFTWARE CONSULTANT INNKEEPER/COUNSELOR
## 17 17
## TELESCOPE SYSTERM SPEC INFORMATION REQUESTED
## 17 15
## OFFICE ADM. PHYSICIAN
## 13 13
## ARCHITECT COMPUTER PROGRAMMER
## 12 12
## OWNER INNKEEPER
## 12 11
## SALES ARCHITECT, FARMER
## 11 10
## COMPUTER/IS ADMINISTRATOR BUILDING CONTRACTOR
## 10 9
## FACTORY PERSONAL ASSISTANT
## 9 9
## CHILD CARE PROVIDER COUNSELOR
## 8 8
## FINANCIAL SOFTWARE CONSLT. MD
## 8 8
## TEACHER/ADMINISTRATOR ASTRONOMER
## 8 7
## CONSULTANT ENGLISH INSTRUCTOR
## 7 7
## INN INNKEEPER-OWNER
## 7 7
## MENTAL HEALTH THERAPIST MERCHANT
## 7 7
## WHOLESALE PRODUCE ARTIST
## 7 6
## EDUCATOR PROJECT MANAGER
## 6 6
## PUBLISHER WHOLESALER/EXPORTER
## 6 6
## FINANCIAL CONSULTANT WHOLESALER
## 5 5
## WRITER ARCHITECHT
## 5 4
## COFFEE FARMER COLLEGE ADMINISTRATOR
## 4 4
## COMPUTER CONSULTANT EXECUTIVE DIRECTOR
## 4 4
## INNKEEPOER LECTURER
## 4 4
## PEDIATRICIAN PHOTOGRAPHER
## 4 4
## PROF PROJECT ENGINEER
## 4 4
## TAX ACCOUNTANT THERAPIST
## 4 4
## ACCOUNT CLERK ACCOUNTING CLERK
## 3 3
## BOOKKEEPER BRANCH MANAGER
## 3 3
## CLERK TYPIST II CLINICAL SOCIAL WORK
## 3 3
## CONSTRUCTION INSPECTOR ENGINEER
## 3 3
## ENTOMOLOGIST FARMER, ATTORNEY, INVESTOR
## 3 3
## GENERAL CONTRACTOR INSTRUCTOR
## 3 3
## KINDERGARTEN TEACHER MACINTOSH TECHNICIAN
## 3 3
## MEDICAL DOCTOR NURSE
## 3 3
## PROPERTY MANAGER REAL ESTATE SALES
## 3 3
## RN SCIENTIST
## 3 3
## VACATION RENTAL OWNER WRITER/EDITOR
## 3 3
## WRITER/EDUCATOR/COUNSELOR 650 IWILEI ROAD, HONOLULU, HI 96817
## 3 2
## ASSISTANT PROFESSOR COLLEGE INSTRUCTOR
## 2 2
## HANDYMAN MANAGER
## 2 2
## PHYSICIAN-DISABLED PILOT
## 2 2
## PRESIDENT PROFESSOR OF BIOLOGY
## 2 2
## PUBLIC RELATIONS SPECIALIST REGIONAL DIRECTOR
## 2 2
## REGISTERED NURSE RESEARCH ASSOCIATE
## 2 2
## RESEARCH ECOLOGIST (Other)
## 2 34## RETIRED ATTORNEY
## 2073 530
## PROFESSOR PHYSICIAN
## 276 234
## NOT EMPLOYED TEACHER
## 232 230
## PRESIDENT
## 205 189
## INFORMATION REQUESTED HOMEMAKER
## 160 155
## CONSULTANT MANAGER
## 114 101
## LAWYER ASTRONOMER
## 69 64
## WRITER PSYCHOLOGIST
## 63 61
## REALTOR SOFTWARE ENGINEER
## 60 59
## RESEARCHER HEALTH CARE
## 56 52
## ARCHITECT COST ESTIMATOR
## 47 47
## ACCOUNTANT ARTIST
## 44 44
## OWNER LAW
## 43 41
## EWO CEO
## 40 36
## SALES PLANNER
## 36 35
## RN STUDENT
## 35 35
## SOCIAL WORKER BOOKSELLER
## 34 32
## ENGINEER MEDIATOR
## 32 32
## SMALL BUSINESS OWNER EDITOR
## 31 30
## ASSISTANT PROFESSOR EDUCATOR
## 29 29
## REAL ESTATE U.S. MARINES
## 29 28
## SPEECH PATHOLOGIST RESEARCH ASSISTANT
## 27 26
## TAX PREPARER ADMINISTRATOR
## 26 25
## INFORMATION SYSTEMS SECURITY MINISTER/PROFESSIONAL MUSICIAN
## 25 25
## MUSIC HOST PARALEGAL
## 25 25
## REAL ESTATE BROKER COMPUTER PROGRAMMER
## 25 24
## EXECUTIVE MECHANIC
## 24 24
## MEDIA PHOTO JOURNALIST
## 24 24
## ADMIN MANAGER INDEPENDENT ASSOCIATE
## 23 23
## INVESTOR MARKETING
## 23 23
## GENERAL MANAGER PROGRAM MANAGER
## 22 21
## REAL ESTATE DEVELOPER ACADEMIC COORDINATOR
## 21 20
## SENIOR FORECASTER WRITER/PRODUCER
## 20 20
## FLIGHT ATTENDANT NURSE
## 19 19
## OFFICER REFUSED
## 19 19
## BANKER SALES/MARKETING/OWNER
## 18 18
## ADMIN. MANAGER ART DIRECTOR
## 17 17
## DOCUMENTARY FILMMAKER ENGLISH PROFESSOR
## 17 17
## ENVIRONMENTAL CONSULTANT INVESTIGATOR
## 17 17
## SELF VACATION RENTAL
## 17 17
## ANALYST GEOSPATIAL INFORMATION TECHNOLOGIST
## 16 16
## PILOT SECRETARY
## 16 16
## SUBSTITUTE TEACHER AIR TRAFFIC CONTROL SPECIALIST
## 16 15
## AIR TRAFFIC CONTROLLER CLERICAL
## 15 15
## CPA DIRECTOR
## 15 15
## GRADUATE ASSISTANT PEDIATRICIAN
## 15 15
## REGISTERED NURSE REGISTRAR
## 15 15
## ADMINISTRATIVE DIRECTIOR CEO AND OWNER
## 14 14
## EDUCATION ADMINISTRATION GRAPHIC ARTIST
## 14 14
## (Other) NA's
## 2440 21Running a summary for the occupation type, I see that for Hawaii county the top 5 most present are Retired, Drafting service, Professor, Not Employed and Teacher. While for Honolulu are: Retired, Attorney, Professor, Physician and Not Employed.
Further I will explore the data looking at these professions in particular.
For both counties and in most cities, Retired group are the highest contributors. In Hawaii county this is followed by Drafting service and in Honolulu by Attorney.
The primary election has a higher percentage of contributions. For Hawaii county there is a higher percentage for the general election for 250 amount.
Bivariate Analysis
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?
The countribution amount varies in relation to location: cities with higher population (Hilo, Honolulu) have more contributers and I was expected that.
Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?
There is a strong relation between contb_receipt_am and election_tp: the primary election has a higher percentage of small amounts.
What was the strongest relationship you found?
Although I knew that Hawaii is the favorite place of retirment for many, I was surprised to see this group so strong in larger cities(Hilo, Honolulu) and such a strong relation between contbr_occupation and contb_receipt_amt.
Multivariate Plots Section
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?
Were there any interesting or surprising interactions between features?
OPTIONAL: Did you create any models with your dataset? Discuss the strengths and limitations of your model.
Final Plots and Summary
Plot One
Description One
Most of the contributions are amount of 75 and 100. for Honolulu county 25% of contributions are over 200 while for Hawaii are 100.
Plot two
Description two
Looking at the most loyal financial supporters, by subsetting the data for individuals making over 20 contributions, I found that the two counties have in common three occupations: retired as the largest, followed by not-employed and professor.
Plot Three
Description Three
Looking at the most generous contributions, these come from Honolulu county. For Hawaii county, they are part of the retired group and come from Captain Cook city - which is not one of the largest cities.